Technical AI Solutions Engineer (AI)

Location: London, Greater London, England, United Kingdom

Company

hirify.global is a global investment banking, securities, and investment management firm with offices around the world.

What you will do

• Design, build, deploy, and operate cloud-native AI applications using modern software engineering practices, CI/CD pipelines, and automated testing.
• Partner with business and engineering teams to identify high-impact AI opportunities and translate requirements into scalable architectures, data models, and technical specifications.
• Embed with internal business teams to map workflows, assess agentic automation opportunities, run pilots, measure adoption, and support production handoff.
• Integrate LLM APIs, retrieval-augmented generation, embeddings, vector search, prompt and context management, structured outputs, validation, and evaluation harnesses.
• Apply cloud, DevOps, MLOps, security, observability, lifecycle management, and resilience practices to production AI systems.
• Document solutions, mentor receiving teams, and transfer code, integration patterns, data models, and operational practices into long-term ownership.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related quantitative field.
• 9+ years of hands-on software engineering experience building, deploying, and supporting production applications.
• Strong proficiency in Python, Java, or Go, with sound software engineering, testing, data modelling, and system design skills.
• Experience translating complex business requirements into cloud-optimized architectures and scalable production solutions.
• Extensive experience with AWS, Azure, or GCP, including serverless services, containers, managed services, automated deployment, monitoring, and cloud security.
• Experience integrating AI/ML capabilities into production applications, including RAG pipelines, embeddings, vector search, prompt templates, structured outputs, evaluation datasets, and model monitoring.

Nice to have

• Experience building agentic AI systems with multi-step planning, tool and API calls, state management, permission checks, failure handling, and auditable action trails.
• Experience implementing agent observability, human-in-the-loop approvals, policy checks, incident handoff, and post-action summarization.
• Experience optimizing production AI systems through model routing, retrieval tuning, caching, batching, streaming, asynchronous execution, cost controls, and quality regression testing.

Culture & Benefits

• Training and development opportunities, firmwide networks, and professional growth programs.
• Benefits, wellness, personal finance offerings, and mindfulness programs.
• Commitment to diversity, inclusion, and reasonable accommodations during the recruiting process.